A Modified Lyapunov-Based MEC Offloading Algorithm for Severity-Aware QoS in 5G/B5G IoT Systems
The rapid growth of latency-sensitive and computation-intensive IoT applications in 5G and Beyond-5G (B5G) networks has increased the demand for efficient Multi-access Edge Computing (MEC) offloading strategies. Current MEC frameworks have several limitations: 1) binary QoS modeling without considering deadline violation severity, 2) a lack of severity-aware optimization in IoT applications, 3) insufficient consideration of different task criticality, and 4) poor handling of dynamic latency and energy trade-off in large-scale IoT environments. This study proposes a modified Lyapunov-based severity-aware MEC offloading framework for heterogeneous 5G/B5G IoT systems. The proposed framework utilizes task deadlines, task criticality, queue states, wireless channel conditions, and MEC resource availability as input for adaptive offloading optimization. A QoS Violation Severity Index is introduced to jointly capture deadline violation magnitude and task criticality. Furthermore, severity-aware virtual queues are integrated with a modified Lyapunov Drift-Plus-Penalty optimization framework to dynamically minimize QoS violation severity while balancing latency and energy consumption. Experimental evaluation demonstrates that the proposed framework significantly reduces average task delay to 82 ms, energy consumption to 6.0 mJ, and QoS violation rate to 4.8%, while improving long-term system stability compared with existing MEC offloading approaches in dynamic 5G/B5G IoT environments.